计算机应用与软件2024，Vol.41Issue(12)：201-207,7.DOI:10.3969/j.issn.1000-386x.2024.12.029
面向图谱频繁关系模式挖掘的异质图神经网络
A HETEROGENEOUS GRAPH NEURAL NETWORK FOR MINING FREQUENT RELATION PATTERNS OF KNOWLEDGE GRAPH
段立 1封皓君 1张碧莹1
作者信息
- 1. 海军工程大学电子工程学院 湖北 武汉 430033
- 折叠
摘要
Abstract
Due to the difficulties in modeling knowledge graph with current mining algorithms,aimed at mining frequent relation patterns and distribution of each structure,a graph neural network model was designed to describe the heterogeneous structure within the scope of nodes.The model took relations as the input of node features,retained the original structure information by using the autoencoder and multi-head attention mechanism,and designed the translation layer of feature structure to map the same structure to the same space,so as to obtain the frequent heterogeneous structure.Experiments show that this model can mine the relation patterns and the distribution of each structure in the graph faster.In addition,it has a stable performance in the link prediction task,which verifies the feature expression ability,and is even better than some joint learning models in heterogeneous graphs with many relationship types.关键词
知识图谱/图神经网络/自编码机制/多头注意力机制/特征结构平移层Key words
Knowledge graph/Graph neural network/Autoencoder/Multi-head attention mechanism/Translation layer of feature structure分类
信息技术与安全科学引用本文复制引用
段立,封皓君,张碧莹..面向图谱频繁关系模式挖掘的异质图神经网络[J].计算机应用与软件,2024,41(12):201-207,7.
